Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm
The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider that the i...
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2023
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ftdoajarticles:oai:doaj.org/article:ebab1399f74c4307bc4832352e7f8bc6 2023-09-05T13:23:57+02:00 Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm Yihong Guan Mou Lv Shuyan Li Yanbo Su Shen Dong 2023-07-01T00:00:00Z https://doi.org/10.3390/w15152677 https://doaj.org/article/ebab1399f74c4307bc4832352e7f8bc6 EN eng MDPI AG https://www.mdpi.com/2073-4441/15/15/2677 https://doaj.org/toc/2073-4441 doi:10.3390/w15152677 2073-4441 https://doaj.org/article/ebab1399f74c4307bc4832352e7f8bc6 Water, Vol 15, Iss 2677, p 2677 (2023) sensitivity function water supply network optimal sensor placement multi-objective White Whale Optimization multi-objective optimization Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 article 2023 ftdoajarticles https://doi.org/10.3390/w15152677 2023-08-13T00:34:21Z The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider that the impact is different when a leak occurs in different nodes. In this study, sensitivity functions of different nodes were obtained according to the influence of the leakage of each node on the water distribution network. Combined with the water pressure correlation and water pressure sensitivity between nodes, the monitoring range of monitoring points and the water demand of covering nodes of monitoring points were taken as objective functions to build an optimal layout model. Taking a pipeline network in Qingdao as an example, the model was solved by using multi-objective White Whale Optimization and NSGA-II. By comparing the operation results of the four cases, it was found that the monitoring points found using multi-objective White Whale Optimization show better searching ability in terms of the sensitivity functions of different nodes. Article in Journal/Newspaper White whale Directory of Open Access Journals: DOAJ Articles Water 15 15 2677 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
sensitivity function water supply network optimal sensor placement multi-objective White Whale Optimization multi-objective optimization Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
spellingShingle |
sensitivity function water supply network optimal sensor placement multi-objective White Whale Optimization multi-objective optimization Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Yihong Guan Mou Lv Shuyan Li Yanbo Su Shen Dong Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm |
topic_facet |
sensitivity function water supply network optimal sensor placement multi-objective White Whale Optimization multi-objective optimization Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
description |
The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider that the impact is different when a leak occurs in different nodes. In this study, sensitivity functions of different nodes were obtained according to the influence of the leakage of each node on the water distribution network. Combined with the water pressure correlation and water pressure sensitivity between nodes, the monitoring range of monitoring points and the water demand of covering nodes of monitoring points were taken as objective functions to build an optimal layout model. Taking a pipeline network in Qingdao as an example, the model was solved by using multi-objective White Whale Optimization and NSGA-II. By comparing the operation results of the four cases, it was found that the monitoring points found using multi-objective White Whale Optimization show better searching ability in terms of the sensitivity functions of different nodes. |
format |
Article in Journal/Newspaper |
author |
Yihong Guan Mou Lv Shuyan Li Yanbo Su Shen Dong |
author_facet |
Yihong Guan Mou Lv Shuyan Li Yanbo Su Shen Dong |
author_sort |
Yihong Guan |
title |
Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm |
title_short |
Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm |
title_full |
Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm |
title_fullStr |
Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm |
title_full_unstemmed |
Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm |
title_sort |
optimized sensor placement of water supply network based on multi-objective white whale optimization algorithm |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/w15152677 https://doaj.org/article/ebab1399f74c4307bc4832352e7f8bc6 |
genre |
White whale |
genre_facet |
White whale |
op_source |
Water, Vol 15, Iss 2677, p 2677 (2023) |
op_relation |
https://www.mdpi.com/2073-4441/15/15/2677 https://doaj.org/toc/2073-4441 doi:10.3390/w15152677 2073-4441 https://doaj.org/article/ebab1399f74c4307bc4832352e7f8bc6 |
op_doi |
https://doi.org/10.3390/w15152677 |
container_title |
Water |
container_volume |
15 |
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15 |
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2677 |
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1776204526988034048 |